Earthquake forecasting and its verification

No proven method is currently available for the reliable short time prediction of earthquakes (minutes to months). However, it is possible to make probabilistic hazard assessments for earthquake risk. These are primarily based on the association of small earthquakes with future large earthquakes. In this paper we discuss a new approach to earthquake forecasting. This approach is based on a pattern informatics (PI) method which quantifies temporal variations in seismicity. The output is a map of areas in a seismogenic region (``hotspots'') where earthquakes are forecast to occur in a future 10-year time span. This approach has been successfully applied to California, to Japan, and on a worldwide basis. These forecasts are binary--an earthquake is forecast either to occur or to not occur. The standard approach to the evaluation of a binary forecast is the use of the relative operating characteristic (ROC) diagram, which is a more restrictive test and less subject to bias than maximum likelihood tests. To test our PI method, we made two types of retrospective forecasts for California. The first is the PI method and the second is a relative intensity (RI) forecast based on the hypothesis that future earthquakes will occur where earthquakes have occurred in the recent past. While both retrospective forecasts are for the ten year period 1 January 2000 to 31 December 2009, we performed an interim analysis 5 years into the forecast. The PI method out performs the RI method under most circumstances.Comment: 10(+1) pages, 5 figures, 2 tables. Submitted to Nonlinearl Processes in Geophysics on 5 August 200

Computational Thinking Integration into Middle Grades Science Classrooms: Strategies for Meeting the Challenges

This paper reports findings from the efforts of a university-based research team as they worked with middle school educators within formal school structures to infuse computer science principles and computational thinking practices. Despite the need to integrate these skills within regular classroom practices to allow all students the opportunity to learn these essential 21st Century skills, prior practice has been to offer these learning experiences outside of mainstream curricula where only a subset of students have access. We have sought to leverage elements of the research-practice partnership framework to achieve our project objectives of integrating computer science and computational thinking within middle science classrooms. Utilizing a qualitative approach to inquiry, we present narratives from three case schools, report on themes across work sites, and share recommendations to guide other practitioners and researchers who are looking to engage in technology-related initiatives to impact the lives of middle grades students

Determining the Effects of Maternal Adiposity on Preterm Neonatal Microbiome and Short Chain Fatty Acid Profiles

The gut microbiota and its metabolites have vast impacts on the human digestive system, immune system, and health outcomes. Short chain volatile fatty acids (SCVFAs) present in feces can be representative of the interactions of the microbiota present in the gut. Low microbiota diversity in the human gut is highly associated with obesity and adverse health outcomes. Furthermore, the maternal microbiome has a direct impact on neonatal microbiota through various pathways such as environment, skin flora, breast milk composition, and vaginal secretions. This study is aimed to further understand the associations between various factors (maternal adiposity, gestational time, length of life, delivery mode, and race/ethnicity ) and neonatal microbiome and its metabolites, SCFA. Data (pre-pregnancy BMI, gestational time, length of life at time of sample collection, delivery mode, race/ethnicity, SCVFA profiles, fecal fermentation profiles, and 16s rRNA sequences, n=75) was obtained from 75 mother-infant dyads. Qiagen CLC Genomics Workbench was used to process 16s RNA data, generate quantitative and qualitative measures of alpha and beta diversity, and generate an analysis of the composition of microbiomes for differential abundances. Multiple metrics were analyzed for alpha and beta diversity and no significant differences were found for acetic acid (A), propionic acid (P), butyric acid (B), or APB combined. Shannon diversity index, a measure of Alpha diversity, showed no significant difference between groups in each subset. BMI differences were significant for no c-section vs. c-section and Black vs. White race/ethnicity. There were no significant differences found in PERMANOVA, a measure of beta diversity, or found in differential abundances among the groups

Read my points: Effect of animation type when speech-reading from EMA data

Three popular vocal-tract animation paradigms were tested for intelligibility when displaying videos of pre-recorded Electromagnetic Articulography (EMA) data in an online experiment. EMA tracks the position of sensors attached to the tongue. The conditions were dots with tails (where only the coil location is presented), 2D animation (where the dots are connected to form 2D representations of the lips, tongue surface and chin), and a 3D model with coil locations driving facial and tongue rigs. The 2D animation (recorded in VisArtico) showed the highest identification of the prompts

Predicting success in medical school: a longitudinal study of common Australian student selection tools

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.Background: Medical student selection and assessment share an underlying high stakes context with the need for valid and reliable tools. This study examined the predictive validity of three tools commonly used in Australia: previous academic performance (Grade Point Average (GPA)), cognitive aptitude (a national admissions test), and non-academic qualities of prospective medical students (interview). Methods: A four year retrospective cohort study was conducted at Flinders University Australia involving 382 graduate entry medical students first enrolled between 2006 and 2009. The main outcomes were academic and clinical performance measures and an indicator of unimpeded progress across the four years of the course. Results: A combination of the selection criteria explained between 7.1 and 29.1 % of variance in performance depending on the outcome measure. Weighted GPA consistently predicted performance across all years of the course. The national admissions test was associated with performance in Years 1 and 2 (pre-clinical) and the interview with performance in Years 3 and 4 (clinical). Those students with higher GPAs were more likely to have unimpeded progress across the entire course (OR = 2.29, 95 % CI 1.57, 3.33). Conclusions: The continued use of multiple selection criteria to graduate entry medical courses is supported, with GPA remaining the single most consistent predictor of performance across all years of the course. The national admissions test is more valuable in the pre-clinical years, and the interview in the clinical years. Future selections research should develop the fledgling research base regarding the predictive validity of the Graduate Australian Medical School Admissions Test (GAMSAT), the algorithms for how individual tools are combined in selection, and further explore the usefulness of the unimpeded progress index

Examining Latina/o Students’ Experiences of Injustice: LatCrit Insights from a Texas High School

We used Latina/Latino Critical Race Theory (LatCrit) to re-analyze survey and interview data from earlier research in which we found Latina/o students reported less positive experiences than other students in this high school. We found racial injustice in class enrollments, in students’ experiences with stereotypes and prejudice, in student-teacher relationships, and in school policies and norms. LatCrit principles illustrate interconnections among racism, interest convergence, and colorblindness that create racial injustice for Latinas/os. We argue that counterstorytelling could emerge to resist that injustice and that educators must understand how racism functions in their schools and interrogate relevant policies and norms

Human and murine IFIT1 proteins do not restrict infection of negative-sense RNA viruses of the Orthomyxoviridae, Bunyaviridae, and Filoviridae families

UNLABELLED: Interferon-induced protein with tetratricopeptide repeats 1 (IFIT1) is a host protein with reported cell-intrinsic antiviral activity against several RNA viruses. The proposed basis for the activity against negative-sense RNA viruses is the binding to exposed 5\u27-triphosphates (5\u27-ppp) on the genome of viral RNA. However, recent studies reported relatively low binding affinities of IFIT1 for 5\u27-ppp RNA, suggesting that IFIT1 may not interact efficiently with this moiety under physiological conditions. To evaluate the ability of IFIT1 to have an impact on negative-sense RNA viruses, we infected Ifit1(-/-) and wild-type control mice and primary cells with four negative-sense RNA viruses (influenza A virus [IAV], La Crosse virus [LACV], Oropouche virus [OROV], and Ebola virus) corresponding to three distinct families. Unexpectedly, a lack of Ifit1 gene expression did not result in increased infection by any of these viruses in cell culture. Analogously, morbidity, mortality, and viral burdens in tissues were identical between Ifit1(-/-) and control mice after infection with IAV, LACV, or OROV. Finally, deletion of the human IFIT1 protein in A549 cells did not affect IAV replication or infection, and reciprocally, ectopic expression of IFIT1 in HEK293T cells did not inhibit IAV infection. To explain the lack of antiviral activity against IAV, we measured the binding affinity of IFIT1 for RNA oligonucleotides resembling the 5\u27 ends of IAV gene segments. The affinity for 5\u27-ppp RNA was approximately 10-fold lower than that for non-2\u27-O-methylated (cap 0) RNA oligonucleotides. Based on this analysis, we conclude that IFIT1 is not a dominant restriction factor against negative-sense RNA viruses. IMPORTANCE: Negative-sense RNA viruses, including influenza virus and Ebola virus, have been responsible for some of the most deadly outbreaks in recent history. The host interferon response and induction of antiviral genes contribute to the control of infections by these viruses. IFIT1 is highly induced after virus infection and reportedly has antiviral activity against several RNA and DNA viruses. However, its role in restricting infection by negative-sense RNA viruses remains unclear. In this study, we evaluated the ability of IFIT1 to inhibit negative-sense RNA virus replication and pathogenesis both in vitro and in vivo. Detailed cell culture and animal studies demonstrated that IFIT1 is not a dominant restriction factor against three different families of negative-sense RNA viruses